SoK: Practical Aspects of Releasing Differentially Private Graphs
New systemization tackles the complex trade-offs between privacy and utility when releasing sensitive network data.
A new research paper provides a crucial roadmap for applying strong privacy protections to sensitive graph data, which is increasingly common in social networks, financial transactions, and communication systems. Authored by Nicholas D'Silva, Surya Nepal, and Salil S. Kanhere, the 20-page 'Systematization of Knowledge (SoK): Practical Aspects of Releasing Differentially Private Graphs' tackles a major practical challenge: how to release useful graph analytics while guaranteeing the privacy of individuals and their relationships through Differential Privacy (DP). The complex, interconnected nature of graphs makes applying DP difficult, often forcing a stark trade-off between data utility and privacy strength.
To solve this, the researchers conducted a comprehensive survey of existing DP graph methods and identified critical vulnerabilities in current approaches. Their key contribution is a novel, practitioner-oriented framework designed to guide the selection, interpretation, and sound evaluation of these methods based on specific project objectives. They demonstrate the framework's use through two detailed scenarios where they assume the role of a social network analyst, applying it to evaluate state-of-the-art techniques. This work, accepted for presentation at the ACM ASIA Conference on Computer and Communications Security (ASIA CCS '26), ultimately provides a unified benchmark and decision-making tool for professionals working with sensitive relational data.
- Provides a comprehensive survey and systematization of over a decade of research on differentially private graph release methods.
- Identifies key vulnerabilities and interpretability issues in existing DP-for-graphs approaches that can lead to misleading privacy claims.
- Introduces a practical, objective-based framework to help practitioners select and evaluate methods, demonstrated with two social network analyst scenarios.
Why It Matters
Enables safer sharing and analysis of sensitive network data (social, financial, health) without compromising individual privacy.